A Hybrid Model of Bidirectional GRU and Graph Neural Network for Multivariate Temporal Data Interpolation

Hongtao Song, Siao Li, Qilong Han

Abstract: In the field of big data, the phenomenon of missing data is widespread, particularly pronounced in time-series datasets, where its impact on subsequent processing is significant. However, existing data imputation methods have not comprehensively and adequately addressed the bidirectional spatio-temporal dependencies within the data, especially the dependencies among different dimensions at various timestamps in multivariate time-series data. To address this issue, this paper proposes a hybrid model that integrates Graph Neural Networks (GNNs) and Bidirectional Recurrent Neural Networks (RNNs) for imputing missing values in multivariate time-series data. The model introduces an enhanced bidirectional Gated Recurrent Unit (GRU) to capture the bidirectional temporal dependencies in the data. Additionally, it employs graph structure learning and attention mechanisms to construct a correlation graph, aggregating inter-dimensional correlation information through graph neural networks. Experimental results indicate that our model has significantly improved the imputation accuracy on three public datasets, particularly under conditions of low missing rates.

External IDs:dblp:conf/ijcnn/SongLH25